title: Improving activity recognition using temporal coherence.
abstract: Assessment of daily physical activity using data from wearable sensors has recently become a prominent research area in the biomedical engineering field and a substantial application for pattern recognition. In this paper, we present an accelerometer-based activity recognition scheme on the basis of a hierarchical structured classifier. A first step consists of distinguishing static activities from dynamic ones in order to extract relevant features for each activity type. Next, a separate classifier is applied to detect more specific activities of the same type. On top of our activity recognition system, we introduce a novel approach to take into account the temporal coherence of activities. Inter-activity transition information is modeled by a directed graph Markov chain. Confidence measures in activity classes are then evaluated from conventional classifier's outputs and coupled with the graph to reinforce activity estimation. Accurate results and significant improvement of activity detection are obtained when applying our system for the recognition of 9 activities for 48 subjects.
authors.forename: Abbas
authors.initials: A
authors.lastname: Ataya
authors.forename: Pierre
authors.initials: P
authors.lastname: Jallon
authors.forename: Pascal
authors.initials: P
authors.lastname: Bianchi
authors.forename: Maeva
authors.initials: M
authors.lastname: Doron
mesh_terms: D000054:Acceleration; D000203:Activities of Daily Living; D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D018670:Monitoring, Ambulatory / Q000379:methods*; D009068:Movement; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013997:Time Factors; D055815:Young Adult